From a49066d4bc5e830bbcade540e810ee1e3fdcfd3d Mon Sep 17 00:00:00 2001 From: Fabien Maussion Date: Thu, 4 Jan 2024 16:03:28 +0000 Subject: [PATCH 1/8] Add mb params perturbation utility func --- oggm/core/massbalance.py | 56 +++++++++++++++++++++++++++++++++++++++ oggm/tests/test_prepro.py | 13 +++++++++ 2 files changed, 69 insertions(+) diff --git a/oggm/core/massbalance.py b/oggm/core/massbalance.py index b5e5394c3..62467df3c 100644 --- a/oggm/core/massbalance.py +++ b/oggm/core/massbalance.py @@ -1890,6 +1890,62 @@ def to_minimize(x, model_attr): return df +@entity_task(log, writes=['mb_calib']) +def perturbate_mb_params(gdir, perturbation=None, reset_default=False): + """Replaces pre-calibrated MB params with perturbed ones for this glacier. + + It simply replaces the existing `mb_calib.json` file with an + updated one with perturbed parameters. The original ones + are stored in the file for re-use after perturbation. + + Users can change the following 4 parameters: + - 'melt_f': unit [kg m-2 day-1 K-1], the melt factor + - 'prcp_fac': unit [-], the precipitation factor + - 'temp_bias': unit [K], the temperature correction applied to the timeseries + - 'bias': unit [mm we yr-1], *substracted* from the computed MB. Rarely used. + + All parameter perturbations are additive, i.e. the value + provided by the user is added to the *precalibrated* value. + For example, `temp_bias=1` means that the temp_bias used by the + model will be the precalibrated one, plus 1 Kelvin. + + The only exception is prpc_fac, which is multiplicative. + For example prcp_fac=1 will leave the precalibrated prcp_fac unchanged, + while 2 will double it. + + Parameters + ---------- + perturbation : dict + the parameters to change and the associated value (see doc above) + reset_default : bool + reset the parameters to their original value + """ + + df = gdir.read_json('mb_calib') + + # Save original params if not there + if 'bias_orig' not in df: + for k in ['bias', 'melt_f', 'prcp_fac', 'temp_bias']: + df[k + '_orig'] = df[k] + + if reset_default: + for k in ['bias', 'melt_f', 'prcp_fac', 'temp_bias']: + df[k] = df[k + '_orig'] + gdir.write_json(df, 'mb_calib') + return df + + for k, v in perturbation.items(): + if k == 'prcp_fac': + df[k] = df[k + '_orig'] * v + elif k in ['bias', 'melt_f', 'temp_bias']: + df[k] = df[k + '_orig'] + v + else: + raise InvalidParamsError(f'Perturbation not valid: {k}') + + gdir.write_json(df, 'mb_calib') + return df + + def _check_terminus_mass_flux(gdir, fls): # Check that we have done this correctly rho = cfg.PARAMS['ice_density'] diff --git a/oggm/tests/test_prepro.py b/oggm/tests/test_prepro.py index 7e85bc9db..844a94e92 100644 --- a/oggm/tests/test_prepro.py +++ b/oggm/tests/test_prepro.py @@ -1663,6 +1663,19 @@ def test_mb_calibration_from_scalar_mb(self): assert pdf['melt_f'] < cfg.PARAMS['melt_f'] assert pdf['prcp_fac'] == cfg.PARAMS['prcp_fac_max'] + # Test perturbate + massbalance.perturbate_mb_params(gdir, perturbation={'temp_bias': -1, + 'prcp_fac': 2}) + pdf = gdir.read_json('mb_calib') + assert pdf['temp_bias'] == cfg.PARAMS['temp_bias_min'] - 1 + assert pdf['prcp_fac'] == cfg.PARAMS['prcp_fac_max'] * 2 + + massbalance.perturbate_mb_params(gdir, reset_default=True) + pdf = gdir.read_json('mb_calib') + assert pdf['temp_bias'] == cfg.PARAMS['temp_bias_min'] + assert pdf['melt_f'] < cfg.PARAMS['melt_f'] + assert pdf['prcp_fac'] == cfg.PARAMS['prcp_fac_max'] + @pytest.mark.slow def test_mb_calibration_from_scalar_mb_multiple_fl(self): From 0835e5779c2830463e860bd8daf1f536532f74e5 Mon Sep 17 00:00:00 2001 From: Fabien Maussion Date: Fri, 5 Jan 2024 20:03:48 +0000 Subject: [PATCH 2/8] also allow to change glen a and fs for perturbation --- oggm/core/flowline.py | 34 ++++++++++++++++-------- oggm/tests/test_models.py | 56 ++++++++++++++++++--------------------- 2 files changed, 49 insertions(+), 41 deletions(-) diff --git a/oggm/core/flowline.py b/oggm/core/flowline.py index 98167b67d..b79b9a334 100644 --- a/oggm/core/flowline.py +++ b/oggm/core/flowline.py @@ -1328,8 +1328,7 @@ def run_until_and_store(self, y1, surface_h_previous[fl_id] = fl.surface_h if 'flux_divergence' in ovars_fl and (yr > self.y0): # calculated after the formula dhdt = mb + flux_div - val = ds['dhdt_myr'].data[j, :] -\ - ds['climatic_mb_myr'].data[j, :] + val = ds['dhdt_myr'].data[j, :] - ds['climatic_mb_myr'].data[j, :] # special treatment for retreating: If the glacier # terminus is getting ice free it means the # climatic mass balance is more negative than the @@ -3173,6 +3172,7 @@ def decide_evolution_model(evolution_model=None): def flowline_model_run(gdir, output_filesuffix=None, mb_model=None, ys=None, ye=None, zero_initial_glacier=False, init_model_fls=None, store_monthly_step=False, + glen_a_fac=None, fs_fac=None, fixed_geometry_spinup_yr=None, store_model_geometry=None, store_fl_diagnostics=None, @@ -3209,6 +3209,13 @@ def flowline_model_run(gdir, output_filesuffix=None, mb_model=None, store_monthly_step : bool whether to store the diagnostic data at a monthly time step or not (default is yearly) + glen_a_fac : float + perturbate the default glen_a (either from the inversion if + PARAMS['use_inversion_params_for_run'] is True or PARAMS['glen_a'] if + False) with a chosen multiplicative parameter. Useful for + parameter perturbation experiments. + fs_fac : float + same as glen_a_fac but for sliding store_model_geometry : bool whether to store the full model geometry run file to disk or not. (new in OGGM v1.4.1: default is to follow @@ -3270,6 +3277,11 @@ def flowline_model_run(gdir, output_filesuffix=None, mb_model=None, fs = cfg.PARAMS['fs'] glen_a = cfg.PARAMS['glen_a'] + if glen_a_fac is not None: + glen_a *= glen_a_fac + if fs_fac is not None: + fs *= fs_fac + kwargs.setdefault('fs', fs) kwargs.setdefault('glen_a', glen_a) @@ -3320,11 +3332,11 @@ def flowline_model_run(gdir, output_filesuffix=None, mb_model=None, 'to prevent this error.') model = evolution_model(fls, mb_model=mb_model, y0=ys, - inplace=True, - is_tidewater=gdir.is_tidewater, - is_lake_terminating=gdir.is_lake_terminating, - water_level=water_level, - **kwargs) + inplace=True, + is_tidewater=gdir.is_tidewater, + is_lake_terminating=gdir.is_lake_terminating, + water_level=water_level, + **kwargs) with warnings.catch_warnings(): # For operational runs we ignore the warnings @@ -3429,7 +3441,7 @@ def run_random_climate(gdir, nyears=1000, y0=None, halfsize=15, if false, every model year will be chosen from the random climate period with the same probability kwargs : dict - kwargs to pass to the FluxBasedModel instance + kwargs to pass to the flowline_model_run task """ mb_model = MultipleFlowlineMassBalance(gdir, @@ -3546,7 +3558,7 @@ def run_constant_climate(gdir, nyears=1000, y0=None, halfsize=15, list of flowlines to use to initialize the model (the default is the present_time_glacier file from the glacier directory) kwargs : dict - kwargs to pass to the FluxBasedModel instance + kwargs to pass to the flowline_model_run task """ mb_model = MultipleFlowlineMassBalance(gdir, @@ -3661,14 +3673,14 @@ def run_from_climate_data(gdir, ys=None, ye=None, min_ys=None, max_ys=None, multiply a factor to the precipitation time series (note that this factor is multiplied to any factor that was decided during calibration or by global parameters) - kwargs : dict - kwargs to pass to the FluxBasedModel instance fixed_geometry_spinup_yr : int if set to an integer, the model will artificially prolongate all outputs of run_until_and_store to encompass all time stamps starting from the chosen year. The only output affected are the glacier wide diagnostic files - all other outputs are set to constants during "spinup" + kwargs : dict + kwargs to pass to the flowline_model_run task """ if init_model_filesuffix is not None: diff --git a/oggm/tests/test_models.py b/oggm/tests/test_models.py index 343f1fa55..a5189fb1d 100644 --- a/oggm/tests/test_models.py +++ b/oggm/tests/test_models.py @@ -311,7 +311,6 @@ class TestInitFlowlineOtherGlacier: def test_define_divides(self, class_case_dir): from oggm.core import centerlines - from oggm.core import climate from oggm.core import inversion from oggm.core import gis from oggm import GlacierDirectory @@ -393,8 +392,6 @@ def test_past_mb_model(self, hef_gdir): gdir = hef_gdir init_present_time_glacier(gdir) - df = gdir.read_json('mb_calib') - # Climate period yrp = [1851, 2000] @@ -1709,9 +1706,8 @@ def test_run(self, class_case_dir): surface_h_previous_timestep = model.fls[0].surface_h # smooth flux divergence where glacier is getting ice free has_become_ice_free = np.logical_and( - np.isclose(model.fls[0].thick, 0.), - dhdt_ref[-1] < 0. - ) + np.isclose(model.fls[0].thick, 0.), + dhdt_ref[-1] < 0.) flux_divergence_ref.append( (dhdt_ref[-1] - climatic_mb_ref[-1]) * np.where(has_become_ice_free, 0.1, 1.)) @@ -2787,7 +2783,6 @@ def test_equilibrium_glacier_wide(self, hef_gdir, inversion_params): glen_a=inversion_params['inversion_glen_a']) init_present_time_glacier(hef_gdir) - fls = hef_gdir.read_pickle('model_flowlines') model = FluxBasedModel(fls, mb_model=mb_mod, y0=0., fs=inversion_params['inversion_fs'], @@ -3370,7 +3365,7 @@ def test_run_dynamic_spinup(self, hef_gdir, minimise_for): for err_msg, kwarg_dyn_spn in error_settings.items(): # test that error is thrown - ignore_errors=False + ignore_errors = False with pytest.raises(RuntimeError, match=err_msg): run_dynamic_spinup( @@ -3390,8 +3385,7 @@ def test_run_dynamic_spinup(self, hef_gdir, minimise_for): filesuffix='_dynamic_spinup', )) # check that it passes with ignore_errors=True - ignore_errors=True - + ignore_errors = True model = run_dynamic_spinup( hef_gdir, minimise_for=minimise_for, @@ -3448,7 +3442,7 @@ def test_run_dynamic_spinup(self, hef_gdir, minimise_for): # test parameter and spinup_start_yr_max, should override spinup_period # to start at spinup_start_yr_max - model_dynamic_spinup_max_start_yr = run_dynamic_spinup( + run_dynamic_spinup( hef_gdir, spinup_period=5, spinup_start_yr=None, @@ -4568,7 +4562,6 @@ def test_hydro_out_commitment(self, hef_gdir, inversion_params, store_monthly_hy # that it works here assert_allclose(odf['tot_prcp'], odf['tot_prcp'].iloc[0]) - # Glacier area is the same (remove on_area?) assert_allclose(odf['on_area'], odf['area_m2']) @@ -5034,7 +5027,7 @@ def test_hydro_out_random(self, hef_gdir, inversion_params, store_monthly_hydro) # check if melt on glacier is always above or equal zero assert np.all(odf['melt_on_glacier'] >= 0) - #@pytest.mark.slow + @pytest.mark.slow @pytest.mark.parametrize('mb_type', ['random', 'const', 'hist']) @pytest.mark.parametrize('mb_bias', [500, -500, 0]) def test_hydro_monhly_vs_annual(self, hef_gdir, inversion_params, @@ -5667,7 +5660,7 @@ def test_distribute(self, hef_elev_gdir, inversion_params): # This can be done without any run from oggm.sandbox import distribute_2d distribute_2d.add_smoothed_glacier_topo(hef_elev_gdir) - tasks.distribute_thickness_per_altitude(hef_elev_gdir); + tasks.distribute_thickness_per_altitude(hef_elev_gdir) distribute_2d.assign_points_to_band(hef_elev_gdir) mb_mod = massbalance.RandomMassBalance(hef_elev_gdir, y0=1980, @@ -5690,7 +5683,7 @@ def test_distribute(self, hef_elev_gdir, inversion_params): fp = hef_elev_gdir.get_filepath('gridded_simulation', filesuffix='_commit') with xr.open_dataset(fp) as ds: thick = ds.distributed_thickness.load() - + fp = hef_elev_gdir.get_filepath('gridded_data') with xr.open_dataset(fp) as ds: ds = ds.load() @@ -5725,30 +5718,33 @@ def test_distribute(self, hef_elev_gdir, inversion_params): yr = 2030 plt.figure() f, ax = plt.subplots() - fl_diag.sel(time=yr)['volume_m3'].plot(ax=ax); - fl_diag.sel(time=yr)['volume_m3_dis'].plot(ax=ax); - plt.show(); plt.figure(); + fl_diag.sel(time=yr)['volume_m3'].plot(ax=ax) + fl_diag.sel(time=yr)['volume_m3_dis'].plot(ax=ax) + plt.figure() f, ax = plt.subplots() - fl_diag.sel(time=yr)['area_m2'].plot(ax=ax); - fl_diag.sel(time=yr)['area_m2_dis'].plot(ax=ax); - plt.show(); plt.figure(); + fl_diag.sel(time=yr)['area_m2'].plot(ax=ax) + fl_diag.sel(time=yr)['area_m2_dis'].plot(ax=ax) + plt.figure() f, ax = plt.subplots() - ds_diag.area_m2.plot(ax=ax); - area_dis.plot(ax=ax); - plt.show(); plt.figure(); + ds_diag.area_m2.plot(ax=ax) + area_dis.plot(ax=ax) + plt.figure() f, ax = plt.subplots() - ds_diag.volume_m3.plot(ax=ax); - vol_dis.plot(ax=ax); - plt.show(); + ds_diag.volume_m3.plot(ax=ax) + vol_dis.plot(ax=ax) + plt.show() - if False: + if do_plot: from matplotlib import animation - import matplotlib; matplotlib.use("TkAgg"); + import matplotlib + matplotlib.use("TkAgg") # Get a handle on the figure and the axes fig, ax = plt.subplots() # Plot the initial frame. cax = thick.isel(time=0).plot(add_colorbar=True, cmap='viridis', - vmin=0, vmax=350, cbar_kwargs={'extend': 'neither'}) + vmin=0, vmax=350, + cbar_kwargs={'extend': 'neither'}) + def animate(frame): cax.set_array(thick.values[frame, :].flatten()) animation.FuncAnimation(fig, animate, frames=len(thick.time), From 31ce9447ebcb46d10fb800669a3a15a6b78c6259 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 15 Dec 2023 01:48:54 +0000 Subject: [PATCH 3/8] Bump actions/upload-artifact from 3 to 4 Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 3 to 4. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] --- .github/workflows/run-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index 1b0c42f6d..a1184b57a 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -45,7 +45,7 @@ jobs: run: ./ci/run_tests.sh "${{ matrix.test-env }}" "${{ matrix.container }}" - name: Upload pytest-mpl artifacts if: failure() - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: pytest-mpl-results path: /tmp/oggm-mpl-results/ From 2452541c6fb0b9be70453e0cde15ef2b9f0b910a Mon Sep 17 00:00:00 2001 From: rebneugebauer <98671933+rebneugebauer@users.noreply.github.com> Date: Fri, 5 Jan 2024 21:07:39 +0100 Subject: [PATCH 4/8] Installing OGGM on Windows (#1666) Co-authored-by: Anouk Vlug --------- Co-authored-by: Fabien Maussion Co-authored-by: Anouk Vlug --- .../windows_installation/check_wsl.png | Bin 0 -> 13406 bytes 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not work on Windows. However, there is a workaround using the Windows +Subsystem for Linux (WSL). There is no official support for installing OGGM on +Windows. Nevertheless, the steps listed hereafter have been used for a successful +installation, but there is no guarantee that it will work for everyone. + +Install WSL +----------- +Enable Intel Virtual Technology +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +As a first step, you need to enable ‘Intel Virtual Technology’ to allow WSL to be +executed in the next step. To do this, restart your computer and access the BIOS or +UEFI settings by one of the following keys: F2, F12, Esc, Del. Which key you need +to use depends on your computer. With the arrow key you are now able to navigate to +the ‘configuration’ tab. Select ‘Intel Virtual Technology’ and change the settings +from ‘disabled’ to ‘enabled’. Save the change and exit the BIOS or UEFI settings by +using F10. + +.. figure:: _static/windows_installation/enable_intel_virtual_technology.png + :width: 100% + + +Enable WSL +~~~~~~~~~~ + +Next you need to enable WSL. This is possible through the Windows Features dialog +or Power Shell. In the Windows search bar, type 'features' to bring up the +**Turn Windows Features on or off** dialog. Scroll down and check **Windows +Subsystem for Linux**. It is also necessary to tick **Virtual Machine Platform** +and **Windows Hypervisor Platform** to avoid errors. Click ‘OK’ and in the prompted +new field ‘install’. + +.. figure:: _static/windows_installation/windows_features_on_off.png + :width: 40% +| + +It is also possible to enable WSL as administrator via the Power Shell by using the +following command:: + + Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux + +Afterwards you will be asked to restart Windows. + +Check WSL +~~~~~~~~~ + +To be sure WSL is enabled you can check it by opening a Command Prompt and type ’WSL’ +after restarting Windows: + +.. figure:: _static/windows_installation/check_wsl.png + :width: 50% + +This means, WLS in enabled but you have not installed a Linux distribution yet. + +Download Windows Subsystem for Linux and a Linux distribution of your choice +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Download the free +`Windows subsystem for Linux `_ +in the Microsoft Store. With that you keep Windows as main operating system and are able +work with Linux alongside your Windows application. There are some limitations compared +to a complete second operating system, but it is sufficient to install OGGM. In addition, +download and install `Ubuntu `_ +or another Linux distribution of your choice. + +UNIX User Account +~~~~~~~~~~~~~~~~~ + +Now open Ubuntu. You will automatically be asked to create a UNIX account. + +.. figure:: _static/windows_installation/unix_user_account.png + :width: 75% + +Installing OGGM +--------------- + +After successfully setting up Linux as a Windows subsystem, you can proceed with the OGGM +installation process. There are multiple approaches available for `installing OGGM `_, +and all of them should work with WSL. However, we are aware that this may not always be +the case. Here, we provide an example of steps that have resulted in a successful installation. + +1. Install the `prerequisites `_ +for creating a python environment. + +2. Create a `python environment `_ +using a yml file. + +3. `Install OGGM `_ +with ``pip install oggm`` + +4. `Test `_ if the installation was successful. + +5. **Congrats**, you are now set-up for the `getting started `_ section! + +If you encounter problems, check out the +`trouble shooting `_ documentation. From 567b0c939694f34ac9e31f16ff40febf22b89e73 Mon Sep 17 00:00:00 2001 From: Patrick Schmitt <50843444+pat-schmitt@users.noreply.github.com> Date: Wed, 10 Jan 2024 10:24:12 +0100 Subject: [PATCH 5/8] Small dynamic spinup adaptations (#1671) --- docs/whats-new.rst | 9 +- oggm/core/dynamic_spinup.py | 296 ++++++++++++++++++------------------ oggm/params.cfg | 2 +- oggm/tests/test_models.py | 16 +- 4 files changed, 166 insertions(+), 157 deletions(-) diff --git a/docs/whats-new.rst b/docs/whats-new.rst index a2b55b57e..8fe53f2ff 100644 --- a/docs/whats-new.rst +++ b/docs/whats-new.rst @@ -9,14 +9,19 @@ v1.6.2 (unreleased) Enhancements ~~~~~~~~~~~~ -- There is now a possibility for initializing a elevation-band flowline using +- There is now a possibility for initializing a elevation-band flowline using external thickness data and conduct a dynamic run with it (:pull:`1658`). + By `Patrick Schmitt `_ +- The default minimum thickness for the dynamic spinup was changed from 10 m + to 2 m. The new value was found in a local study and makes a larger + difference for smaller (thinner) glaciers (:pull:`1667`). + By `Patrick Schmitt `_ Bug fixes ~~~~~~~~~ - The binned variables in the elevation band flowlines did not use the - glacier mask when preserving the total values. This is a bad + glacier mask when preserving the total values. This is a bad bug that is now fixed (:pull:`1661`). By `Patrick Schmitt `_ diff --git a/oggm/core/dynamic_spinup.py b/oggm/core/dynamic_spinup.py index 56de21eb2..e2184d1e0 100644 --- a/oggm/core/dynamic_spinup.py +++ b/oggm/core/dynamic_spinup.py @@ -37,11 +37,11 @@ def run_dynamic_spinup(gdir, init_model_filesuffix=None, init_model_yr=None, target_yr=None, target_value=None, minimise_for='area', precision_percent=1, precision_absolute=1, min_ice_thickness=None, - first_guess_t_bias=-2, t_bias_max_step_length=2, + first_guess_t_spinup=-2, t_spinup_max_step_length=2, maxiter=30, output_filesuffix='_dynamic_spinup', store_model_geometry=True, store_fl_diagnostics=None, store_model_evolution=True, ignore_errors=False, - return_t_bias_best=False, ye=None, + return_t_spinup_best=False, ye=None, model_flowline_filesuffix='', add_fixed_geometry_spinup=False, **kwargs): """Dynamically spinup the glacier to match area or volume at the RGI date. @@ -137,13 +137,13 @@ def run_dynamic_spinup(gdir, init_model_filesuffix=None, init_model_yr=None, the forward model run. Therefore you could see quite fast changes (spikes) in the time-evolution (especially visible in length and area). If you set this value to 0 the filtering can be switched off. - Default is 10. - first_guess_t_bias : float + Default is cfg.PARAMS['dynamic_spinup_min_ice_thick']. + first_guess_t_spinup : float The initial guess for the temperature bias for the spinup MassBalanceModel in °C. Default is -2. - t_bias_max_step_length : float - Defines the maximums allowed change of t_bias between two iterations. + t_spinup_max_step_length : float + Defines the maximums allowed change of t_spinup between two iterations. Is needed to avoid to large changes. Default is 2. maxiter : int @@ -171,7 +171,7 @@ def run_dynamic_spinup(gdir, init_model_filesuffix=None, init_model_yr=None, the 'output_filesuffix', if an error during the dynamic spinup occurs. This is useful if you want to keep glaciers for the following tasks. Default is True - return_t_bias_best : bool + return_t_spinup_best : bool If True the used temperature bias for the spinup is returned in addition to the final model. If an error occurs and ignore_error=True, the returned value is np.nan. @@ -338,7 +338,7 @@ def save_model_without_dynamic_spinup(): 'climate data (if yr_climate_start > target_yr)') if ignore_errors: model_dynamic_spinup_end = save_model_without_dynamic_spinup() - if return_t_bias_best: + if return_t_spinup_best: return model_dynamic_spinup_end, np.nan else: return model_dynamic_spinup_end @@ -374,7 +374,7 @@ def save_model_without_dynamic_spinup(): if reference_value == 0.: if ignore_errors: model_dynamic_spinup_end = save_model_without_dynamic_spinup() - if return_t_bias_best: + if return_t_spinup_best: return model_dynamic_spinup_end, np.nan else: return model_dynamic_spinup_end @@ -391,11 +391,11 @@ def save_model_without_dynamic_spinup(): forward_model_runs = [0] # the actual spinup run - def run_model_with_spinup_to_target_year(t_bias): + def run_model_with_spinup_to_target_year(t_spinup): forward_model_runs.append(forward_model_runs[-1] + 1) - # with t_bias the glacier state after spinup is changed between iterations - mb_model_spinup.temp_bias = t_bias + # with t_spinup the glacier state after spinup is changed between iterations + mb_model_spinup.temp_bias = t_spinup # run the spinup model_spinup = evolution_model(copy.deepcopy(fls_spinup), mb_model_spinup, @@ -456,10 +456,10 @@ def run_model_with_spinup_to_target_year(t_bias): else: raise NotImplementedError(f'{cost_var}') - def cost_fct(t_bias, model_dynamic_spinup_end_loc, other_variable_mismatch_loc): + def cost_fct(t_spinup, model_dynamic_spinup_end_loc, other_variable_mismatch_loc): # actual model run model_value, other_value, model_dynamic_spinup, ice_free = \ - run_model_with_spinup_to_target_year(t_bias) + run_model_with_spinup_to_target_year(t_spinup) # save the final model for later model_dynamic_spinup_end_loc.append(copy.deepcopy(model_dynamic_spinup)) @@ -475,18 +475,18 @@ def init_cost_fct(): model_dynamic_spinup_end_loc = [] other_variable_mismatch_loc = [] - def c_fct(t_bias): - return cost_fct(t_bias, model_dynamic_spinup_end_loc, + def c_fct(t_spinup): + return cost_fct(t_spinup, model_dynamic_spinup_end_loc, other_variable_mismatch_loc) return c_fct, model_dynamic_spinup_end_loc, other_variable_mismatch_loc def minimise_with_spline_fit(fct_to_minimise): - # defines limits of t_bias in accordance to maximal allowed change + # defines limits of t_spinup in accordance to maximal allowed change # between iterations - t_bias_limits = [first_guess_t_bias - t_bias_max_step_length, - first_guess_t_bias + t_bias_max_step_length] - t_bias_guess = [] + t_spinup_limits = [first_guess_t_spinup - t_spinup_max_step_length, + first_guess_t_spinup + t_spinup_max_step_length] + t_spinup_guess = [] mismatch = [] # this two variables indicate that the limits were already adapted to # avoid an ice_free or out_of_domain error @@ -494,13 +494,13 @@ def minimise_with_spline_fit(fct_to_minimise): was_out_of_domain = False was_errors = [was_out_of_domain, was_ice_free] - def get_mismatch(t_bias): - t_bias = copy.deepcopy(t_bias) - # first check if the new t_bias is in limits - if t_bias < t_bias_limits[0]: + def get_mismatch(t_spinup): + t_spinup = copy.deepcopy(t_spinup) + # first check if the new t_spinup is in limits + if t_spinup < t_spinup_limits[0]: # was the smaller limit already executed, if not first do this - if t_bias_limits[0] not in t_bias_guess: - t_bias = copy.deepcopy(t_bias_limits[0]) + if t_spinup_limits[0] not in t_spinup_guess: + t_spinup = copy.deepcopy(t_spinup_limits[0]) else: # smaller limit was already used, check if it was # already newly defined with glacier exceeding domain @@ -511,12 +511,12 @@ def get_mismatch(t_bias): f'{np.min(np.abs(mismatch))}%') else: # ok we set a new lower limit - t_bias_limits[0] = t_bias_limits[0] - \ - t_bias_max_step_length - elif t_bias > t_bias_limits[1]: + t_spinup_limits[0] = (t_spinup_limits[0] - + t_spinup_max_step_length) + elif t_spinup > t_spinup_limits[1]: # was the larger limit already executed, if not first do this - if t_bias_limits[1] not in t_bias_guess: - t_bias = copy.deepcopy(t_bias_limits[1]) + if t_spinup_limits[1] not in t_spinup_guess: + t_spinup = copy.deepcopy(t_spinup_limits[1]) else: # larger limit was already used, check if it was # already newly defined with ice free glacier @@ -527,19 +527,19 @@ def get_mismatch(t_bias): f'{np.min(np.abs(mismatch))}%') else: # ok we set a new upper limit - t_bias_limits[1] = t_bias_limits[1] + \ - t_bias_max_step_length + t_spinup_limits[1] = (t_spinup_limits[1] + + t_spinup_max_step_length) - # now clip t_bias with limits - t_bias = np.clip(t_bias, t_bias_limits[0], t_bias_limits[1]) + # now clip t_spinup with limits + t_spinup = np.clip(t_spinup, t_spinup_limits[0], t_spinup_limits[1]) # now start with mismatch calculation # if error during spinup (ice_free or out_of_domain) this defines - # how much t_bias is changed to look for an error free glacier spinup - t_bias_search_change = t_bias_max_step_length / 10 + # how much t_spinup is changed to look for an error free glacier spinup + t_spinup_search_change = t_spinup_max_step_length / 10 # maximum number of changes to look for an error free glacier - max_iterations = int(t_bias_max_step_length / t_bias_search_change) + max_iterations = int(t_spinup_max_step_length / t_spinup_search_change) is_out_of_domain = True is_ice_free_spinup = True is_ice_free_end = True @@ -553,57 +553,57 @@ def get_mismatch(t_bias): while ((is_out_of_domain | is_ice_free_spinup | is_ice_free_end) & (iteration < max_iterations)): try: - tmp_mismatch, is_ice_free_spinup = fct_to_minimise(t_bias) + tmp_mismatch, is_ice_free_spinup = fct_to_minimise(t_spinup) # no error occurred, so we are not outside the domain is_out_of_domain = False # check if we are ice_free after spinup, if so we search - # for a new upper limit for t_bias + # for a new upper limit for t_spinup if is_ice_free_spinup: was_errors[1] = True define_new_upper_limit = True # special treatment if it is the first guess - if np.isclose(t_bias, first_guess_t_bias) & \ + if np.isclose(t_spinup, first_guess_t_spinup) & \ doing_first_guess: is_first_guess_ice_free = True # here directly jump to the smaller limit - t_bias = copy.deepcopy(t_bias_limits[0]) + t_spinup = copy.deepcopy(t_spinup_limits[0]) elif is_first_guess_ice_free & doing_first_guess: # make large steps if it is first guess - t_bias = t_bias - t_bias_max_step_length + t_spinup = t_spinup - t_spinup_max_step_length else: - t_bias = np.round(t_bias - t_bias_search_change, - decimals=1) - if np.isclose(t_bias, t_bias_guess).any(): + t_spinup = np.round(t_spinup - t_spinup_search_change, + decimals=1) + if np.isclose(t_spinup, t_spinup_guess).any(): iteration = copy.deepcopy(max_iterations) # check if we are ice_free at the end of the model run, if - # so we use the lower t_bias limit and change the limit if + # so we use the lower t_spinup limit and change the limit if # needed elif np.isclose(tmp_mismatch, -100.): is_ice_free_end = True was_errors[1] = True define_new_upper_limit = True # special treatment if it is the first guess - if np.isclose(t_bias, first_guess_t_bias) & \ + if np.isclose(t_spinup, first_guess_t_spinup) & \ doing_first_guess: is_first_guess_ice_free = True # here directly jump to the smaller limit - t_bias = copy.deepcopy(t_bias_limits[0]) + t_spinup = copy.deepcopy(t_spinup_limits[0]) elif is_first_guess_ice_free & doing_first_guess: # make large steps if it is first guess - t_bias = t_bias - t_bias_max_step_length + t_spinup = t_spinup - t_spinup_max_step_length else: # if lower limit was already used change it and use - if t_bias == t_bias_limits[0]: - t_bias_limits[0] = t_bias_limits[0] - \ - t_bias_max_step_length - t_bias = copy.deepcopy(t_bias_limits[0]) + if t_spinup == t_spinup_limits[0]: + t_spinup_limits[0] = (t_spinup_limits[0] - + t_spinup_max_step_length) + t_spinup = copy.deepcopy(t_spinup_limits[0]) else: - # otherwise just try with a colder t_bias - t_bias = np.round(t_bias - t_bias_search_change, - decimals=1) + # otherwise just try with a colder t_spinup + t_spinup = np.round(t_spinup - t_spinup_search_change, + decimals=1) else: is_ice_free_end = False @@ -612,23 +612,23 @@ def get_mismatch(t_bias): # check if glacier grow to large if 'Glacier exceeds domain boundaries, at year:' in f'{e}': # ok we where outside the domain, therefore we search - # for a new lower limit for t_bias in 0.1 °C steps + # for a new lower limit for t_spinup in 0.1 °C steps is_out_of_domain = True define_new_lower_limit = True was_errors[0] = True # special treatment if it is the first guess - if np.isclose(t_bias, first_guess_t_bias) & \ + if np.isclose(t_spinup, first_guess_t_spinup) & \ doing_first_guess: is_first_guess_out_of_domain = True # here directly jump to the larger limit - t_bias = t_bias_limits[1] + t_spinup = t_spinup_limits[1] elif is_first_guess_out_of_domain & doing_first_guess: # make large steps if it is first guess - t_bias = t_bias + t_bias_max_step_length + t_spinup = t_spinup + t_spinup_max_step_length else: - t_bias = np.round(t_bias + t_bias_search_change, - decimals=1) - if np.isclose(t_bias, t_bias_guess).any(): + t_spinup = np.round(t_spinup + t_spinup_search_change, + decimals=1) + if np.isclose(t_spinup, t_spinup_guess).any(): iteration = copy.deepcopy(max_iterations) else: @@ -646,9 +646,9 @@ def get_mismatch(t_bias): # free run msg = 'Not able to conduct one error free run. Error is ' if is_first_guess_ice_free: - msg += f'"ice_free" with last t_bias of {t_bias}.' + msg += f'"ice_free" with last t_spinup of {t_spinup}.' elif is_first_guess_out_of_domain: - msg += f'"out_of_domain" with last t_bias of {t_bias}.' + msg += f'"out_of_domain" with last t_spinup of {t_spinup}.' else: raise RuntimeError('Something unexpected happened!') @@ -664,13 +664,13 @@ def get_mismatch(t_bias): 'free glacier after spinup! Best mismatch ' f'{np.min(np.abs(mismatch))}%') elif is_ice_free_end: - raise RuntimeError('Not able to find a t_bias so that ' + raise RuntimeError('Not able to find a t_spinup so that ' 'glacier is not ice free at the end! ' - '(Last t_bias ' - f'{t_bias + t_bias_max_step_length} °C)') + '(Last t_spinup ' + f'{t_spinup + t_spinup_max_step_length} °C)') else: raise RuntimeError('Something unexpected happened during ' - 'definition of new t_bias limits!') + 'definition of new t_spinup limits!') else: # if we found a new limit set it if define_new_upper_limit & define_new_lower_limit: @@ -678,71 +678,71 @@ def get_mismatch(t_bias): # a to large step was_errors[0] = False was_errors[1] = False - if t_bias <= t_bias_limits[0]: - t_bias_limits[0] = t_bias - t_bias_limits[1] = t_bias_limits[0] + \ - t_bias_max_step_length - elif t_bias >= t_bias_limits[1]: - t_bias_limits[1] = t_bias - t_bias_limits[0] = t_bias_limits[1] - \ - t_bias_max_step_length + if t_spinup <= t_spinup_limits[0]: + t_spinup_limits[0] = t_spinup + t_spinup_limits[1] = (t_spinup_limits[0] + + t_spinup_max_step_length) + elif t_spinup >= t_spinup_limits[1]: + t_spinup_limits[1] = t_spinup + t_spinup_limits[0] = (t_spinup_limits[1] - + t_spinup_max_step_length) else: if is_first_guess_ice_free: - t_bias_limits[1] = t_bias + t_spinup_limits[1] = t_spinup elif is_out_of_domain: - t_bias_limits[0] = t_bias + t_spinup_limits[0] = t_spinup else: raise RuntimeError('I have not expected to get here!') elif define_new_lower_limit: - t_bias_limits[0] = copy.deepcopy(t_bias) - if t_bias >= t_bias_limits[1]: + t_spinup_limits[0] = copy.deepcopy(t_spinup) + if t_spinup >= t_spinup_limits[1]: # this happens when the first guess was out of domain was_errors[0] = False - t_bias_limits[1] = t_bias_limits[0] + \ - t_bias_max_step_length + t_spinup_limits[1] = (t_spinup_limits[0] + + t_spinup_max_step_length) elif define_new_upper_limit: - t_bias_limits[1] = copy.deepcopy(t_bias) - if t_bias <= t_bias_limits[0]: + t_spinup_limits[1] = copy.deepcopy(t_spinup) + if t_spinup <= t_spinup_limits[0]: # this happens when the first guess was ice free was_errors[1] = False - t_bias_limits[0] = t_bias_limits[1] - \ - t_bias_max_step_length + t_spinup_limits[0] = (t_spinup_limits[1] - + t_spinup_max_step_length) - return float(tmp_mismatch), float(t_bias) + return float(tmp_mismatch), float(t_spinup) # first guess - new_mismatch, new_t_bias = get_mismatch(first_guess_t_bias) - t_bias_guess.append(new_t_bias) + new_mismatch, new_t_spinup = get_mismatch(first_guess_t_spinup) + t_spinup_guess.append(new_t_spinup) mismatch.append(new_mismatch) if abs(mismatch[-1]) < precision_percent: - return t_bias_guess, mismatch + return t_spinup_guess, mismatch # second (arbitrary) guess is given depending on the outcome of first - # guess, when mismatch is 100% t_bias is changed for - # t_bias_max_step_length, but at least the second guess is 0.2 °C away + # guess, when mismatch is 100% t_spinup is changed for + # t_spinup_max_step_length, but at least the second guess is 0.2 °C away # from the first guess step = np.sign(mismatch[-1]) * max(np.abs(mismatch[-1]) * - t_bias_max_step_length / 100, + t_spinup_max_step_length / 100, 0.2) - new_mismatch, new_t_bias = get_mismatch(t_bias_guess[0] + step) - t_bias_guess.append(new_t_bias) + new_mismatch, new_t_spinup = get_mismatch(t_spinup_guess[0] + step) + t_spinup_guess.append(new_t_spinup) mismatch.append(new_mismatch) if abs(mismatch[-1]) < precision_percent: - return t_bias_guess, mismatch + return t_spinup_guess, mismatch # Now start with splin fit for guessing - while len(t_bias_guess) < maxiter: + while len(t_spinup_guess) < maxiter: # get next guess from splin (fit partial linear function to previously - # calculated (mismatch, t_bias) pairs and get t_bias value where + # calculated (mismatch, t_spinup) pairs and get t_spinup value where # mismatch=0 from this fitted curve) sort_index = np.argsort(np.array(mismatch)) tck = interpolate.splrep(np.array(mismatch)[sort_index], - np.array(t_bias_guess)[sort_index], + np.array(t_spinup_guess)[sort_index], k=1) - # here we catch interpolation errors (two different t_bias with - # same mismatch), could happen if one t_bias was close to a newly + # here we catch interpolation errors (two different t_spinup with + # same mismatch), could happen if one t_spinup was close to a newly # defined limit if np.isnan(tck[1]).any(): if was_errors[0]: @@ -759,14 +759,14 @@ def get_mismatch(t_bias): 'unknown, need to check by hand! Best ' 'mismatch ' f'{np.min(np.abs(mismatch))}%') - new_mismatch, new_t_bias = get_mismatch(float(interpolate.splev(0, + new_mismatch, new_t_spinup = get_mismatch(float(interpolate.splev(0, tck) )) - t_bias_guess.append(new_t_bias) + t_spinup_guess.append(new_t_spinup) mismatch.append(new_mismatch) if abs(mismatch[-1]) < precision_percent: - return t_bias_guess, mismatch + return t_spinup_guess, mismatch # Ok when we end here the spinup could not find satisfying match after # maxiter(ations) @@ -827,7 +827,7 @@ def get_mismatch(t_bias): # try to conduct minimisation, if an error occurred try shorter spinup # period try: - final_t_bias_guess, final_mismatch = minimise_with_spline_fit(c_fun) + final_t_spinup_guess, final_mismatch = minimise_with_spline_fit(c_fun) # ok no error occurred so we succeeded break except RuntimeError as e: @@ -840,7 +840,7 @@ def get_mismatch(t_bias): f'The error message of the dynamic spinup is: {e}') if ignore_errors: model_dynamic_spinup_end = save_model_without_dynamic_spinup() - if return_t_bias_best: + if return_t_spinup_best: return model_dynamic_spinup_end, np.nan else: return model_dynamic_spinup_end @@ -863,7 +863,7 @@ def get_mismatch(t_bias): # also save some other stuff gdir.add_to_diagnostics('temp_bias_dynamic_spinup', - float(final_t_bias_guess[-1])) + float(final_t_spinup_guess[-1])) gdir.add_to_diagnostics('dynamic_spinup_target_year', int(target_yr)) gdir.add_to_diagnostics('dynamic_spinup_period', @@ -891,8 +891,8 @@ def get_mismatch(t_bias): diag_path=diag_path, fl_diag_path=fl_diag_path, ) - if return_t_bias_best: - return model_dynamic_spinup_end[-1], final_t_bias_guess[-1] + if return_t_spinup_best: + return model_dynamic_spinup_end[-1], final_t_spinup_guess[-1] else: return model_dynamic_spinup_end[-1] @@ -931,7 +931,7 @@ def dynamic_melt_f_run_with_dynamic_spinup( minimise_for='area', climate_input_filesuffix='', spinup_period=20, min_spinup_period=10, target_yr=None, precision_percent=1, precision_absolute=1, min_ice_thickness=None, - first_guess_t_bias=-2, t_bias_max_step_length=2, maxiter=30, + first_guess_t_spinup=-2, t_spinup_max_step_length=2, maxiter=30, store_model_geometry=True, store_fl_diagnostics=None, local_variables=None, set_local_variables=False, do_inversion=True, spinup_start_yr_max=None, add_fixed_geometry_spinup=True, @@ -1026,14 +1026,14 @@ def dynamic_melt_f_run_with_dynamic_spinup( the forward model run. Therefore you could see quite fast changes (spikes) in the time-evolution (especially visible in length and area). If you set this value to 0 the filtering can be switched off. - Default is 10. - first_guess_t_bias : float + Default is cfg.PARAMS['dynamic_spinup_min_ice_thick']. + first_guess_t_spinup : float The initial guess for the temperature bias for the spinup MassBalanceModel in °C. Default is -2. - t_bias_max_step_length : float - Defines the maximums allowed change of t_bias between two iterations. Is - needed to avoid to large changes. + t_spinup_max_step_length : float + Defines the maximums allowed change of t_spinup between two iterations. + Is needed to avoid to large changes. Default is 2 maxiter : int Maximum number of minimisation iterations per dynamic spinup where area @@ -1054,9 +1054,9 @@ def dynamic_melt_f_run_with_dynamic_spinup( Default is None (-> raise an error if no dict is provided) set_local_variables : bool If True this resets the local_variables to their initial state. It - sets the first_guess_t_bias with the key 't_bias' AND sets the current - glacier volume with the key 'vol_m3_ref' which is later used in the - calibration during inversion. + sets the first_guess_t_spinup with the key 't_spinup' AND sets the + current glacier volume with the key 'vol_m3_ref' which is later used in + the calibration during inversion. Default is False do_inversion : bool If True a complete inversion is conducted using the provided melt_f @@ -1093,7 +1093,7 @@ def dynamic_melt_f_run_with_dynamic_spinup( if set_local_variables: # clear the provided dictionary and set the first elements local_variables.clear() - local_variables['t_bias'] = [first_guess_t_bias] + local_variables['t_spinup'] = [first_guess_t_spinup] # ATTENTION: it is assumed that the flowlines in gdir have the volume # we want to match during calibrate_inversion_from_consensus when we # set_local_variables @@ -1166,7 +1166,7 @@ def dynamic_melt_f_run_with_dynamic_spinup( # do not ignore errors in dynamic spinup, so all 'bad'/intermediate files # are deleted in run_dynamic_spinup function try: - model, last_best_t_bias = run_dynamic_spinup( + model, last_best_t_spinup = run_dynamic_spinup( gdir, continue_on_error=False, # force to raise an error in @entity_task add_to_log_file=False, # dont write to log file in @entity_task @@ -1182,13 +1182,13 @@ def dynamic_melt_f_run_with_dynamic_spinup( precision_percent=precision_percent, precision_absolute=precision_absolute, min_ice_thickness=min_ice_thickness, - t_bias_max_step_length=t_bias_max_step_length, + t_spinup_max_step_length=t_spinup_max_step_length, maxiter=maxiter, minimise_for=minimise_for, - first_guess_t_bias=local_variables['t_bias'][-1], + first_guess_t_spinup=local_variables['t_spinup'][-1], output_filesuffix=output_filesuffix, store_model_evolution=True, ignore_errors=False, - return_t_bias_best=True, ye=ye, + return_t_spinup_best=True, ye=ye, store_model_geometry=store_model_geometry, store_fl_diagnostics=store_fl_diagnostics, model_flowline_filesuffix=model_flowline_filesuffix, @@ -1198,7 +1198,7 @@ def dynamic_melt_f_run_with_dynamic_spinup( # as we expect we are not so far away in the next iteration (only # needed for optimisation, potentially need less iterations in # run_dynamic_spinup) - local_variables['t_bias'].append(last_best_t_bias) + local_variables['t_spinup'].append(last_best_t_spinup) except RuntimeError as e: raise RuntimeError(f'Dynamic spinup raised error! (Message: {e})') @@ -1221,8 +1221,8 @@ def dynamic_melt_f_run_with_dynamic_spinup_fallback( mb_model_historical=None, mb_model_spinup=None, climate_input_filesuffix='', spinup_period=20, min_spinup_period=10, target_yr=None, precision_percent=1, - precision_absolute=1, min_ice_thickness=10, - first_guess_t_bias=-2, t_bias_max_step_length=2, maxiter=30, + precision_absolute=1, min_ice_thickness=None, + first_guess_t_spinup=-2, t_spinup_max_step_length=2, maxiter=30, store_model_geometry=True, store_fl_diagnostics=None, do_inversion=True, spinup_start_yr_max=None, add_fixed_geometry_spinup=True, **kwargs): @@ -1311,13 +1311,13 @@ def dynamic_melt_f_run_with_dynamic_spinup_fallback( the forward model run. Therefore you could see quite fast changes (spikes) in the time-evolution (especially visible in length and area). If you set this value to 0 the filtering can be switched off. - Default is 10. - first_guess_t_bias : float + Default is cfg.PARAMS['dynamic_spinup_min_ice_thick']. + first_guess_t_spinup : float The initial guess for the temperature bias for the spinup MassBalanceModel in °C. Default is -2. - t_bias_max_step_length : float - Defines the maximums allowed change of t_bias between two iterations. Is + t_spinup_max_step_length : float + Defines the maximums allowed change of t_spinup between two iterations. Is needed to avoid to large changes. Default is 2 maxiter : int @@ -1410,8 +1410,8 @@ def dynamic_melt_f_run_with_dynamic_spinup_fallback( precision_percent=precision_percent, precision_absolute=precision_absolute, min_ice_thickness=min_ice_thickness, - first_guess_t_bias=first_guess_t_bias, - t_bias_max_step_length=t_bias_max_step_length, + first_guess_t_spinup=first_guess_t_spinup, + t_spinup_max_step_length=t_spinup_max_step_length, maxiter=maxiter, output_filesuffix=output_filesuffix, store_model_geometry=store_model_geometry, @@ -1428,18 +1428,6 @@ def dynamic_melt_f_run_with_dynamic_spinup_fallback( f'original melt factor ({melt_f}). Therefore the last ' 'try is to conduct a run until ye without a dynamic ' 'spinup.') - model_end = run_from_climate_data( - gdir, - add_to_log_file=False, - min_ys=yr_clim_min, ye=ye, - output_filesuffix=output_filesuffix, - climate_input_filesuffix=climate_input_filesuffix, - store_model_geometry=store_model_geometry, - store_fl_diagnostics=store_fl_diagnostics, - init_model_fls=fls_init, evolution_model=evolution_model, - fixed_geometry_spinup_yr=ys) - - gdir.add_to_diagnostics('used_spinup_option', 'fixed geometry spinup') # set all dynamic diagnostics to None if there where some successful # runs @@ -1460,6 +1448,22 @@ def dynamic_melt_f_run_with_dynamic_spinup_fallback( gdir.add_to_diagnostics(key, None) gdir.add_to_diagnostics('run_dynamic_spinup_success', False) + + # try to make a fixed geometry spinup + model_end = run_from_climate_data( + gdir, + continue_on_error=False, # force to raise an error in @entity_task + add_to_log_file=False, + min_ys=yr_clim_min, ye=ye, + output_filesuffix=output_filesuffix, + climate_input_filesuffix=climate_input_filesuffix, + store_model_geometry=store_model_geometry, + store_fl_diagnostics=store_fl_diagnostics, + init_model_fls=fls_init, evolution_model=evolution_model, + fixed_geometry_spinup_yr=ys) + + gdir.add_to_diagnostics('used_spinup_option', 'fixed geometry spinup') + return model_end diff --git a/oggm/params.cfg b/oggm/params.cfg index ef631459d..8ce585cab 100644 --- a/oggm/params.cfg +++ b/oggm/params.cfg @@ -358,7 +358,7 @@ use_inversion_params_for_run = True # match area or volume. Only grid points with a larger thickness are considered # to the total area. This is needed to filter out area changes due to climate # variability around the rgi year (spikes). -dynamic_spinup_min_ice_thick = 10. +dynamic_spinup_min_ice_thick = 2. ### Tidewater glaciers options diff --git a/oggm/tests/test_models.py b/oggm/tests/test_models.py index a5189fb1d..9465cf990 100644 --- a/oggm/tests/test_models.py +++ b/oggm/tests/test_models.py @@ -3351,14 +3351,14 @@ def test_run_dynamic_spinup(self, hef_gdir, minimise_for): # first we artificially produce some errors in hef_gdir using extreme kwargs error_settings = { 'Not able to conduct one error free run. Error is "ice_free"': - {'first_guess_t_bias': 100}, + {'first_guess_t_spinup': 100}, 'The difference between the rgi_date and the start year of the ' 'climate data is too small to run a dynamic spinup!': {'min_spinup_period': 300}, 'The given reference value is Zero, no dynamic spinup possible!': {'init_model_fls': fls_zero_ice}, 'Not able to conduct one error free run. Error is "out_of_domain"': - {'first_guess_t_bias': -100}, + {'first_guess_t_spinup': -100}, 'Could not find mismatch smaller 0.1%': {'precision_percent': 0.1} } @@ -3410,19 +3410,19 @@ def test_run_dynamic_spinup(self, hef_gdir, minimise_for): ye = hef_gdir.get_climate_info()['baseline_yr_1'] + 1 precision_percent = 1 precision_absolute = 1 - model_dynamic_spinup_ye, t_bias = run_dynamic_spinup( + model_dynamic_spinup_ye, t_spinup = run_dynamic_spinup( hef_gdir, spinup_period=40, spinup_start_yr=spinup_start_yr, target_yr=yr_rgi, ye=ye, - return_t_bias_best=True, + return_t_spinup_best=True, minimise_for=minimise_for, precision_percent=precision_percent, precision_absolute=precision_absolute, output_filesuffix='_dynamic_spinup_historical', ) - assert isinstance(t_bias, float) + assert isinstance(t_spinup, float) assert model_dynamic_spinup_ye.yr == ye ds = utils.compile_run_output( hef_gdir, input_filesuffix='_dynamic_spinup_historical', path=False) @@ -3595,13 +3595,13 @@ def test_run_dynamic_spinup_special_cases(self, hef_gdir, minimise_for): # check that ignore_error is working correctly ignore_errors = True - model_dynamic_spinup_error, t_bias_best = run_dynamic_spinup( + model_dynamic_spinup_error, t_spinup_best = run_dynamic_spinup( gdir, minimise_for=minimise_for, output_filesuffix='_dynamic_spinup', maxiter=10, ignore_errors=ignore_errors, - return_t_bias_best=True) + return_t_spinup_best=True) # check if model geometry is correctly saved in gdir fp = gdir.get_filepath('model_geometry', @@ -3614,7 +3614,7 @@ def test_run_dynamic_spinup_special_cases(self, hef_gdir, minimise_for): yr_rgi = gdir.rgi_date + 1 # convert to hydro year assert fmod.last_yr == np.clip(yr_rgi, yr_min, None) assert len(model_dynamic_spinup_error.fls) == len(fmod.fls) - assert np.isnan(t_bias_best) + assert np.isnan(t_spinup_best) @pytest.mark.parametrize('do_inversion', [True, False]) @pytest.mark.parametrize('minimise_for', ['area', 'volume']) From c0b6969b82f8fa5a5df4b0cf1a3358336d008f09 Mon Sep 17 00:00:00 2001 From: Fabien Maussion Date: Mon, 22 Jan 2024 10:15:06 +0000 Subject: [PATCH 6/8] Add filesuffix to mb calibration (#1678) --- oggm/core/massbalance.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/oggm/core/massbalance.py b/oggm/core/massbalance.py index 62467df3c..6acfe87e3 100644 --- a/oggm/core/massbalance.py +++ b/oggm/core/massbalance.py @@ -1589,6 +1589,7 @@ def mb_calibration_from_scalar_mb(gdir, *, temp_bias_min=None, temp_bias_max=None, mb_model_class=MonthlyTIModel, + filesuffix='', ): """Determine the mass balance parameters from a scalar mass-balance value. @@ -1684,6 +1685,10 @@ def mb_calibration_from_scalar_mb(gdir, *, temp_bias_max: float the maximum accepted value for the temperature bias during optimisation. Defaults to cfg.PARAMS['temp_bias_max']. + filesuffix: str + add a filesuffix to mb_params.json. This could be useful for sensitivity + analyses with MB models, if they need to fetch other sets of params for + example. """ # Param constraints @@ -1881,12 +1886,12 @@ def to_minimize(x, model_attr): df['baseline_climate_source'] = gdir.get_climate_info()['baseline_climate_source'] # Write if write_to_gdir: - if gdir.has_file('mb_calib') and not overwrite_gdir: + if gdir.has_file('mb_calib', filesuffix=filesuffix) and not overwrite_gdir: raise InvalidWorkflowError('`mb_calib.json` already exists for ' 'this repository. Set `overwrite_gdir` ' 'to True if you want to overwrite ' 'a previous calibration.') - gdir.write_json(df, 'mb_calib') + gdir.write_json(df, 'mb_calib', filesuffix=filesuffix) return df From 1b31f69b3aaffc1f00b89f2055a352b24a1a84fb Mon Sep 17 00:00:00 2001 From: Fabien Maussion Date: Thu, 1 Feb 2024 20:41:35 +0000 Subject: [PATCH 7/8] more flexbility --- oggm/cfg.py | 8 ++++--- oggm/core/massbalance.py | 46 ++++++++++++++++++++++++++++++---------- oggm/tasks.py | 1 + 3 files changed, 41 insertions(+), 14 deletions(-) diff --git a/oggm/cfg.py b/oggm/cfg.py index 1efe78a25..75e60d164 100644 --- a/oggm/cfg.py +++ b/oggm/cfg.py @@ -312,14 +312,16 @@ def set_logging_config(logging_level='INFO'): Print confirmation that things are working as expected, e.g. when each task is run correctly (this is the default). WARNING - Indication that something unexpected happened on a glacier, - but that OGGM is still working on this glacier. + Do not print INFO or DEBUG but print WARNING (which indicate that + something unexpected happened on a glacier but that OGGM is + still working on this glacier). ERROR Print workflow messages and errors only, e.g. when a glacier cannot run properly. WORKFLOW Print only high level, workflow information (typically, one message - per task). Errors and warnings will NOT be printed. + per task). Errors and warnings will NOT be printed. This is the level + we recommend for operational large-scale runs. CRITICAL Print nothing but fatal errors. diff --git a/oggm/core/massbalance.py b/oggm/core/massbalance.py index 6acfe87e3..28fe574da 100644 --- a/oggm/core/massbalance.py +++ b/oggm/core/massbalance.py @@ -320,6 +320,7 @@ class MonthlyTIModel(MassBalanceModel): def __init__(self, gdir, filename='climate_historical', input_filesuffix='', + mb_params_filesuffix='', fl_id=None, melt_f=None, temp_bias=None, @@ -340,7 +341,10 @@ def __init__(self, gdir, set to a different BASENAME if you want to use alternative climate data. Default is 'climate_historical' input_filesuffix : str, optional - append a suffix to the filename (useful for GCM runs). + append a suffix to the climate input filename (useful for GCM runs). + mb_params_filesuffix : str, optional + append a suffix to the mb params calibration file (useful for + sensitivity runs). fl_id : int, optional if this flowline has been calibrated alone and has specific model parameters. @@ -378,6 +382,7 @@ def __init__(self, gdir, super(MonthlyTIModel, self).__init__() self.valid_bounds = [-1e4, 2e4] # in m self.fl_id = fl_id # which flowline are we the model of? + self.mb_params_filesuffix = mb_params_filesuffix # which mb params? self.gdir = gdir if melt_f is None: @@ -543,13 +548,16 @@ def temp_bias(self, new_temp_bias): @lazy_property def calib_params(self): if self.fl_id is None: - return self.gdir.read_json('mb_calib') + return self.gdir.read_json('mb_calib', self.mb_params_filesuffix) else: try: - return self.gdir.read_json('mb_calib', - filesuffix=f'_fl{self.fl_id}') + out = self.gdir.read_json('mb_calib', filesuffix=f'_fl{self.fl_id}') + if self.mb_params_filesuffix: + raise InvalidWorkflowError('mb_params_filesuffix cannot be ' + 'used with multiple flowlines') + return out except FileNotFoundError: - return self.gdir.read_json('mb_calib') + return self.gdir.read_json('mb_calib', self.mb_params_filesuffix) def is_year_valid(self, year): return self.ys <= year <= self.ye @@ -1403,6 +1411,7 @@ def mb_calibration_from_wgms_mb(gdir, **kwargs): Parameters ---------- **kwargs : any kwarg accepted by mb_calibration_from_scalar_mb + except `ref_mb` and `ref_mb_years` """ # Note that this currently does not work for hydro years (WGMS uses hydro) @@ -1428,12 +1437,15 @@ def mb_calibration_from_geodetic_mb(gdir, *, calibrate_param2=None, calibrate_param3=None, mb_model_class=MonthlyTIModel, + filesuffix='', ): """Calibrate for geodetic MB data from Hugonnet et al., 2021. The data table can be obtained with utils.get_geodetic_mb_dataframe(). It is equivalent to the original data from Hugonnet, but has some outlier - values filtered. See `this notebook` for more details. + values filtered. See [this notebook](https://nbviewer.org/urls/ + cluster.klima.uni-bremen.de/~oggm/geodetic_ref_mb/convert_vold1.ipynb) + for more details. The problem of calibrating many unknown parameters on geodetic data is currently unsolved. This is OGGM's current take, based on trial and @@ -1480,6 +1492,10 @@ def mb_calibration_from_geodetic_mb(gdir, *, the MassBalanceModel to use for the calibration. Needs to use the same parameters as MonthlyTIModel (the default): melt_f, temp_bias, prcp_fac. + filesuffix: str + add a filesuffix to mb_params.json. This could be useful for sensitivity + analyses with MB models, if they need to fetch other sets of params for + example. Returns ------- @@ -1551,6 +1567,7 @@ def mb_calibration_from_geodetic_mb(gdir, *, prcp_fac_max=prcp_fac_max, temp_bias=temp_bias, mb_model_class=mb_model_class, + filesuffix=filesuffix, ) else: @@ -1565,6 +1582,7 @@ def mb_calibration_from_geodetic_mb(gdir, *, calibrate_param3=calibrate_param3, temp_bias=temp_bias, mb_model_class=mb_model_class, + filesuffix=filesuffix, ) @@ -1896,7 +1914,7 @@ def to_minimize(x, model_attr): @entity_task(log, writes=['mb_calib']) -def perturbate_mb_params(gdir, perturbation=None, reset_default=False): +def perturbate_mb_params(gdir, perturbation=None, reset_default=False, filesuffix=''): """Replaces pre-calibrated MB params with perturbed ones for this glacier. It simply replaces the existing `mb_calib.json` file with an @@ -1923,9 +1941,15 @@ def perturbate_mb_params(gdir, perturbation=None, reset_default=False): perturbation : dict the parameters to change and the associated value (see doc above) reset_default : bool - reset the parameters to their original value + reset the parameters to their original value. This might be + unnecessary if using the filesuffix mechanism. + filesuffix : str + write the modified parameters in a separate mb_calib.json file + with the filesuffix appended. This can then be read by the + MassBalanceModel for example instead of the default one. + Note that it's always the default, precalibrated params + file which is read to start with. """ - df = gdir.read_json('mb_calib') # Save original params if not there @@ -1936,7 +1960,7 @@ def perturbate_mb_params(gdir, perturbation=None, reset_default=False): if reset_default: for k in ['bias', 'melt_f', 'prcp_fac', 'temp_bias']: df[k] = df[k + '_orig'] - gdir.write_json(df, 'mb_calib') + gdir.write_json(df, 'mb_calib', filesuffix=filesuffix) return df for k, v in perturbation.items(): @@ -1947,7 +1971,7 @@ def perturbate_mb_params(gdir, perturbation=None, reset_default=False): else: raise InvalidParamsError(f'Perturbation not valid: {k}') - gdir.write_json(df, 'mb_calib') + gdir.write_json(df, 'mb_calib', filesuffix=filesuffix) return df diff --git a/oggm/tasks.py b/oggm/tasks.py index 6fc675066..e09a9766f 100644 --- a/oggm/tasks.py +++ b/oggm/tasks.py @@ -32,6 +32,7 @@ from oggm.core.massbalance import mb_calibration_from_wgms_mb from oggm.core.massbalance import apparent_mb_from_linear_mb from oggm.core.massbalance import apparent_mb_from_any_mb +from oggm.core.massbalance import perturbate_mb_params from oggm.core.massbalance import fixed_geometry_mass_balance from oggm.core.massbalance import compute_ela from oggm.shop.w5e5 import process_w5e5_data From b4451a9ab9c0b63b80b66ef1b87d0a684ceb137f Mon Sep 17 00:00:00 2001 From: Fabien Maussion Date: Thu, 1 Feb 2024 21:03:58 +0000 Subject: [PATCH 8/8] fix test --- oggm/core/massbalance.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/oggm/core/massbalance.py b/oggm/core/massbalance.py index b2afaf8a8..79c12f92c 100644 --- a/oggm/core/massbalance.py +++ b/oggm/core/massbalance.py @@ -382,7 +382,7 @@ def __init__(self, gdir, super(MonthlyTIModel, self).__init__() self.valid_bounds = [-1e4, 2e4] # in m self.fl_id = fl_id # which flowline are we the model of? - self.mb_params_filesuffix = mb_params_filesuffix # which mb params? + self._mb_params_filesuffix = mb_params_filesuffix # which mb params? self.gdir = gdir if melt_f is None: @@ -548,16 +548,16 @@ def temp_bias(self, new_temp_bias): @lazy_property def calib_params(self): if self.fl_id is None: - return self.gdir.read_json('mb_calib', self.mb_params_filesuffix) + return self.gdir.read_json('mb_calib', self._mb_params_filesuffix) else: try: out = self.gdir.read_json('mb_calib', filesuffix=f'_fl{self.fl_id}') - if self.mb_params_filesuffix: + if self._mb_params_filesuffix: raise InvalidWorkflowError('mb_params_filesuffix cannot be ' 'used with multiple flowlines') return out except FileNotFoundError: - return self.gdir.read_json('mb_calib', self.mb_params_filesuffix) + return self.gdir.read_json('mb_calib', self._mb_params_filesuffix) def is_year_valid(self, year): return self.ys <= year <= self.ye